Showing papers by "Vincent Zoete published in 2022"

The SwissSimilarity 2021 Web Tool: Novel Chemical Libraries and Additional Methods for an Enhanced Ligand-Based Virtual Screening Experience

Abstract: Hit finding, scaffold hopping, and structure–activity relationship studies are important tasks in rational drug discovery. Implementation of these tasks strongly depends on the availability of compounds similar to a known bioactive molecule. SwissSimilarity is a web tool for low-to-high-throughput virtual screening of multiple chemical libraries to find molecules similar to a compound of interest. According to the similarity principle, the output list of molecules generated by SwissSimilarity is expected to be enriched in compounds that are likely to share common protein targets with the query molecule and that can, therefore, be acquired and tested experimentally in priority. Compound libraries available for screening using SwissSimilarity include approved drugs, clinical candidates, known bioactive molecules, commercially available and synthetically accessible compounds. The first version of SwissSimilarity launched in 2015 made use of various 2D and 3D molecular descriptors, including path-based FP2 fingerprints and ElectroShape vectors. However, during the last few years, new fingerprinting methods for molecular description have been developed or have become popular. Here we would like to announce the launch of the new version of the SwissSimilarity web tool, which features additional 2D and 3D methods for estimation of molecular similarity: extended-connectivity, MinHash, 2D pharmacophore, extended reduced graph, and extended 3D fingerprints. Moreover, it is now possible to screen for molecular structures having the same scaffold as the query compound. Additionally, all compound libraries available for screening in SwissSimilarity have been updated, and several new ones have been added to the list. Finally, the interface of the website has been comprehensively rebuilt to provide a better user experience. The new version of SwissSimilarity is freely available starting from December 2021.

Secondary IDH1 resistance mutations and oncogenic IDH2 mutations cause acquired resistance to ivosidenib in cholangiocarcinoma

Abstract: The mutant IDH1 inhibitor ivosidenib improves outcomes for patients with IDH1-mutated cholangiocarcinoma, but resistance inevitably develops. Mechanisms of resistance and strategies to overcome resistance are poorly understood. Here we describe two patients with IDH1 R132C-mutated metastatic cholangiocarcinoma who developed acquired resistance to ivosidenib. After disease progression, one patient developed an oncogenic IDH2 mutation, and the second patient acquired a secondary IDH1 D279N mutation. To characterize the putative IDH1 resistance mutation, cells expressing the double-mutant were generated. In vitro, IDH1 R132H/D279N produces (R)-2HG less efficiently than IDH1 R132H. However, its binding to ivosidenib is impaired and it retains the ability to produce (R)-2HG and promote cellular transformation in the presence of ivosidenib. The irreversible mutant IDH1 inhibitor LY3410738 binds and blocks (R)-2HG production and cellular transformation by IDH1 R132H/D279N. These resistance mechanisms suggest that IDH1-mutated cholangiocarcinomas remain dependent on (R)-2HG even after prolonged ivosidenib treatment. Sequential mutant IDH inhibitor therapy should be explored as a strategy to overcome acquired resistance to mutant IDH inhibitors.

Machine learning predictions of MHC-II specificities reveal alternative binding mode of class II epitopes

Abstract: CD4+ T cells orchestrate the adaptive immune response against pathogens and cancer by recognizing epitopes presented on MHC-II molecules. The high polymorphism of MHC-II genes represents an important hurdle towards accurate prediction and identification of CD4+ T-cell epitopes in different individuals and different species. Here we collected and curated a dataset of 627,013 unique MHC-II ligands identified by mass spectrometry. This enabled us to precisely determine the binding motifs of 88 MHC-II alleles across human, mouse, cattle and chicken. Analysis of these binding specificities combined with X-ray crystallography refined our understanding of the molecular determinants of MHC-II motifs and revealed a widespread reverse binding mode in MHC-II ligands. We then developed a machine learning framework to accurately predict binding specificities and ligands of any MHC-II allele. This tool improves and expands predictions of CD4+ T-cell epitopes, and enabled us to discover and characterize several viral and bacterial epitopes following the aforementioned reverse binding mode.

Structural Prediction of Peptide-MHC Binding Modes.

Abstract: The immune system is constantly protecting its host from the invasion of pathogens and the development of cancer cells. The specific CD8+ T-cell immune response against virus-infected cells and tumor cells is based on the T-cell receptor recognition of antigenic peptides bound to class I major histocompatibility complexes (MHC) at the surface of antigen presenting cells. Consequently, the peptide binding specificities of the highly polymorphic MHC have important implications for the design of vaccines, for the treatment of autoimmune diseases, and for personalized cancer immunotherapy. Evidence-based machine-learning approaches have been successfully used for the prediction of peptide binders and are currently being developed for the prediction of peptide immunogenicity. However, understanding and modeling the structural details of peptide/MHC binding is crucial for a better understanding of the molecular mechanisms triggering the immunological processes, estimating peptide/MHC affinity using universal physics-based approaches, and driving the design of novel peptide ligands. Unfortunately, due to the large diversity of MHC allotypes and possible peptides, the growing number of 3D structures of peptide/MHC (pMHC) complexes in the Protein Data Bank only covers a small fraction of the possibilities. Consequently, there is a growing need for rapid and efficient approaches to predict 3D structures of pMHC complexes. Here, we review the key characteristics of the 3D structure of pMHC complexes before listing databases and other sources of information on pMHC structures and MHC specificities. Finally, we discuss some of the most prominent pMHC docking software.

Both APRIL and antibody-fragment-based CAR T cells for myeloma induce BCMA downmodulation by trogocytosis and internalization

Abstract: Background Chimeric antigen receptor (CAR) T cell therapy targeting B cell maturation antigen (BCMA) on multiple myeloma (MM) produces fast but not long-lasting responses. Reasons for treatment failure are poorly understood. CARs simultaneously targeting two antigens may represent an alternative. Here, we (1) designed and characterized novel A proliferation inducing ligand (APRIL) based dual-antigen targeting CARs, and (2) investigated mechanisms of resistance to CAR T cells with three different BCMA-binding moieties (APRIL, single-chain-variable-fragment, heavy-chain-only). Methods Three new APRIL-CARs were designed and characterized. Human APRIL-CAR T cells were evaluated for their cytotoxic function in vitro and in vivo, for their polyfunctionality, immune synapse formation, memory, exhaustion phenotype and tonic signaling activity. To investigate resistance mechanisms, we analyzed BCMA levels and cellular localization and quantified CAR T cell–target cell interactions by live microscopy. Impact on pathway activation and tumor cell proliferation was assessed in vitro and in vivo. Results APRIL-CAR T cells in a trimeric ligand binding conformation conferred fast but not sustained antitumor responses in vivo in mouse xenograft models. In vitro trimer-BBζ CAR T cells were more polyfunctional and formed stronger immune synapses than monomer-BBζ CAR T cells. After CAR T cell–myeloma cell contact, BCMA was rapidly downmodulated on target cells with all evaluated binding moieties. CAR T cells acquired BCMA by trogocytosis, and BCMA on MM cells was rapidly internalized. Since BCMA can be re-expressed during progression and persisting CAR T cells may not protect patients from relapse, we investigated whether non-functional CAR T cells play a role in tumor progression. While CAR T cell–MM cell interactions activated BCMA pathway, we did not find enhanced tumor growth in vitro or in vivo. Conclusion Antitumor responses with APRIL-CAR T cells were fast but not sustained. Rapid BCMA downmodulation occurred independently of whether an APRIL or antibody-based binding moiety was used. BCMA internalization mostly contributed to this effect, but trogocytosis by CAR T cells was also observed. Our study sheds light on the mechanisms underlying CAR T cell failure in MM when targeting BCMA and can inform the development of improved treatment strategies.

Structure-based optimization of type III indoleamine 2,3-dioxygenase 1 (IDO1) inhibitors

Abstract: Abstract The haem enzyme indoleamine 2,3-dioxygenase 1 (IDO1) catalyses the rate-limiting step in the kynurenine pathway of tryptophan metabolism and plays an essential role in immunity, neuronal function, and ageing. Expression of IDO1 in cancer cells results in the suppression of an immune response, and therefore IDO1 inhibitors have been developed for use in anti-cancer immunotherapy. Here, we report an extension of our previously described highly efficient haem-binding 1,2,3-triazole and 1,2,4-triazole inhibitor series, the best compound having both enzymatic and cellular IC50 values of 34 nM. We provide enzymatic inhibition data for almost 100 new compounds and X-ray diffraction data for one compound in complex with IDO1. Structural and computational studies explain the dramatic drop in activity upon extension to pocket B, which has been observed in diverse haem-binding inhibitor scaffolds. Our data provides important insights for future IDO1 inhibitor design.

The actin assembly requirements of the formin Fus1 to build the fusion focus

Abstract: Formins form the largest family of actin filament nucleators and elongators, involved in the assembly of diverse actin structures. Actin filament nucleation and elongation activities reside in the formin homology 1 (FH1) and FH2 domains, common to all formins. However, the rate of these reactions varies between formins by at least 20-fold. Typically, each cell expresses several distinct formins, each contributing to the assembly of one or several actin structures, raising the question of what confers each formin its specificity. Here, using the formin Fus1 in the fission yeast Schizosaccharomyces pombe, we systematically probed the importance of formin nucleation and elongation rates for function in vivo. Fus1 assembles the actin fusion focus, an aster-like structure of actin filaments at the contact site between gametes, necessary for the process of cell fusion to form the zygote during sexual reproduction. By constructing chimeric formins with combinations of FH1 and FH2 domains previously characterized in vitro, we establish that changes in formin nucleation and elongation rates have direct consequences on the architecture of the fusion focus, and that Fus1 native high nucleation and low elongation rates are optimal for fusion focus assembly. We further describe a point mutant in the Fus1 FH2 domain that preserves native nucleation and elongation rates in vitro but alters function in vivo, indicating an additional property of the FH2 domain. Thus, rates of actin assembly are tailored for assembly of specific actin structures.

Rapid BCMA downmodulation on myeloma cells upon CAR T cell contact is mediated by trogocytosis and BCMA internalization

Abstract: Background Chimeric antigen receptor (CAR) T cell therapy targeting B cell maturation antigen (BCMA) on multiple myeloma (MM) produces fast but not long-lasting responses. Reasons for treatment failure are poorly understood. CARs simultaneously targeting two antigens may represent an alternative. Here, we (1) designed and characterized novel A proliferation inducing ligand (APRIL) based dual-antigen targeting CARs, and (2) investigated mechanisms of resistance to CAR T cells with three different BCMA-binding moieties (APRIL, single-chain-variable-fragment, heavy-chain-only). Methods Three new APRIL-CARs were designed and characterized. Human APRIL-CAR T cells were evaluated for their cytotoxic function in vitro and in vivo, for their polyfunctionality, immune synapse formation, memory, exhaustion phenotype and tonic signaling activity. To investigate resistance mechanisms, we analyzed BCMA levels and cellular localization and quantified CAR T cell - target cell interactions by live microscopy. Impact on pathway activation and tumor cell proliferation was assessed in vitro and in vivo. Results APRIL-BBζ CAR T cells in a trimeric ligand binding conformation conferred the best polyfunctionality, immune synapse formation and fast anti-tumor function in vivo in two different mouse xenograft models. Upon CAR T cell – myeloma cell contact, we found rapid BCMA downmodulation on target cells with all three evaluated binding moieties. CAR T cells acquired BCMA on their cell surface by trogocytosis, and BCMA on MM cells was rapidly internalized. Since trogocytosis can lead to CAR T cell exhaustion and presence of CAR T cells does not protect patients from relapse, we investigated whether non-functional CAR T cells play a role in tumor progression. While CAR T cell – MM cell interactions activated BCMA pathway, we did not find enhanced tumor growth in vitro or in vivo. Conclusion We designed and characterized distinct APRIL-CAR T cells for dual-antigen targeting of MM. Rapid BCMA downmodulation occurred upon CAR T cell – tumor cell encounter independently of whether an APRIL or antibody-based binding moiety was used. BCMA internalization mostly contributed to this effect, but trogocytosis by CAR T cells was also observed. Our study sheds light on the mechanisms underlying CAR T cell failure in MM and can inform the development of improved treatment strategies.

Structure-based prediction of BRAF mutation classes using machine-learning approaches

Abstract: The BRAF kinase is attracting a lot of attention in oncology as alterations of its amino acid sequence can constitutively activate the MAP kinase signaling pathway, potentially contributing to the malignant transformation of the cell but at the same time rendering it sensitive to targeted therapy. Several pathologic BRAF variants were grouped in three different classes (I, II and III) based on their effects on the protein activity and pathway. Discerning the class of a BRAF mutation permits to adapt the treatment proposed to the patient. However, this information is lacking new and experimentally uncharacterized BRAF mutations detected in a patient biopsy. To overcome this issue, we developed a new in silico tool based on machine learning approaches to predict the potential class of a BRAF missense variant. As class I only involves missense mutations of Val600, we focused on the mutations of classes II and III, which are more diverse and challenging to predict. Using a logistic regression model and features including structural information, we were able to predict the classes of known mutations with an accuracy of 90%. This new and fast predictive tool will help oncologists to tackle potential pathogenic BRAF mutations and to propose the most appropriate treatment for their patients.